PROJECT SUMMARY Oral health was cited as the greatest unmet health need in the United States, greatly affecting the nation's poor children. Access to preventive dental care in all its dimensions, affordability, accessibility, availability, acceptability and accommodation, is a precursor of utilization of preventive care services, which have been shown to be effective in averting caries and severe oral health outcomes. Identifying and advancing interventions to address access to preventive dental care for children requires rigorous modeling to reliably estimate access, to make inferences under uncertainty of factors impacting dental care delivery and to quantify how interventions might change the access to care given limited resources. The proposed objective is to support informed and reliable policy making and interventions for access to dental care for children at the national level. This proposal will establish a rigorous framework for studying access to preventive dental care for children. This framework not only will contribute towards addressing the primary limitations in the existing research for spatial access to dental care for children but also it will provide inferences on interventions to improve access. The proposed framework takes a system approach in modeling access, accounting for constraints in the system including mobility, user choice, willingness to travel, Medicaid participation and acceptance ratios of dental services providers, Medicaid reimbursement policies, congestion and capacity constraints. The access estimates are complemented by statistical inference, employed to identify communities with greatest unmet dental care need. The proposed modeling is further employed to evaluate potential interventions, and analyze the impact that optimal policy changes and network interventions would have on spatial access and outcomes of the overall system, specific population subgroups, and areas of greatest shortage. Because the models are computationally expensive, particularly, when applied to large geographic areas and in the context of statistical inference, we also propose a distributed computing approach to solve the underlying mathematical access model; the distributed computational approach is particularly important in the inference on interventions. The proposed research will build on multiple datasets already acquired by the research team. The primary source of data consists of the Medicaid Analytical eXtract (MAX) claims data for the U.S. acquired from the Centers for Medicare and Medicaid Services (CMS). We will implement the proposed modeling approach to 45 states in the U.S.?states were excluded because of data availability and/or quality limitations.